With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, most networks have poor recognition ability on high resolution images, resulting in blurred boundaries in the segmented building maps. Second, the similarity between buildings and background results in intraclass inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block. Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then, to handle intra-class inconsistency, we construct Spatial Channel Attention Block. It combines context space information and selects more distinguishable features from space and channel. Finally, we propose an improved loss function to enhance the purpose of loss by adding evaluation indicator. Based on our proposed CT-UNet, we achieve 85.33% mean IoU on the Inria dataset, 91.00% mean IoU on the WHU dataset and 83.92% F1-score on the Massachusetts dataset. The results outperform our baseline (U-Net ResNet-34) by 3.76%, exceed Web-Net by 2.24% and surpass HFSA-Unet by 2.17%.
Microstructures are applied in various fields to improve the friction and lubrication of mechanical components. Through-mask electrochemical etching (TMEE) has shown good feasibility in machining microstructures array. However, the machining precision of microstructures gradually decreases with increasing etching depth in TMEE. Localization and uniformity are essential indicators of machining precision in TMEE. Herein, particle assisted through-mask electrochemical etching (PA-TMEE) method was proposed to improve the localization and uniformity. Firstly, a coupled multi-physical field model, including gas-liquid two-phase flow, particle motion, and electrochemical processes, was established and adopted to predict the profiles of micro pits. Secondly, a comparison experiment between PA-TMEE and traditional TMEE was performed. The experimental results show that using PA-TMEE instead of TMEE resulted in improved localization and uniformity of the micro pits array. Then, the paper analyzed the effect of particle diameter and content on micro pits. When the particle diameter was 40 µm, and the particle content was 6 g/L, the maximum etching factor was 2.4. The minimum coefficient of variation of the diameter and depth of micro pits were 3.3% and 5.2%. Finally, The machining mechanism of PA-TMEE was analyzed by Scanning Electron Microscopy and Energy Dispersive Spectrometer.
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